8. Análisis de Información – Diagnóstico
9.1 Ludoaprendizaje como camino de resiliencia
As storm morphology and evolution can be impacted by the amount of HAE as well as the variability of HAE, it is important to understand how these might change as a function of environment. Using a dataset composed of 123 supercells within ~125 km of a WSR-88D across differing environments, significant relationships were determined between thermodynamic, moisture, and shear parameters and hail areal extent inferred at the base-scan level and between these same parameters and the variability of hail extent within supercell storms. This study shows how environmental variability affects the amount of hailfall and its temporal variability in supercell storms.
Overall, a combination of thermodynamic, shear, and moisture parameters were predictive of the mean HAE in tornadic and nontornadic storms, while shear parameters were strongly associated with hail variability in these storms. Predictive equations were developed through multiple linear regression for both mean HAE and HAE variability (Equations 10 - 15). These equations increased confidence that environmental variables may be able to differentiate tornadic from nontornadic storms, even in similar
environments as predictability was substantially decreased with the inclusion of nontornadic supercells in tornadic environments.
Strong differences between environments were seen in mean HAE when examining LFC height, with an increase in LFC height associated with an increase in mean HAE. This variable is likely to be useful for looking at supercell variability between environments in the future. Height of the ambient 0°C level also emerged as differentiating mean HAE among the data subsets. When looking at mean hail variability
among storms, however, there was no one environmental variable that differentiated among the different environments for the subsets examined. Additionally, previous research showed that HAE variability was dependent on whether the storms were tornadic or nontornadic (e.g., Kumjian and Ryzhkov 2008; Van Den Broeke 2016). Results presented here show that HAE variability was not dependent on whether storms were tornadic or nontornadic; one caveat is that results presented here are not directly analogous to previous results, as the method of comparing hail variability is different between these studies.
Results support previous research (e.g., Rasmussen and Straka 1998; Gilmore et al. 2004; Van Den Broeke 2014) as an increase in MUCAPE was associated with an increase in the mean HAE of storms. Additionally, there were significant differences in HAE across MUCAPE environments for all storms, providing observational evidence that hail production increases with stronger updrafts. There is also evidence that an increase in low-level shear produces an increase in mean HAE, which supports previous modeling studies (e.g., Gilmore et al. 2004; Van Den Broeke et al. 2010). One possible explanation for this result is that ice particles from nearby storms could have been advected and lofted into the region of the analyzed storm. However, when examining low-level shear and hail variability, results seem to contradict previous modeling studies. Adlerman and Droegemeier (2005) implied that an increase in shear values should lead to a reduction in hail variability as the mesocyclone cycling process will slow down and terminate with an increase in shear. Van Den Broeke (2010), however, showed that with the inclusion of ice-microphysics, hail should become more variable with higher shear.
Even though mean HAE and hail variability can be correlated to individual environmental parameters, as shown, it is important to remember that there is strong interdependence among several of the parameters included in the analysis, as noted earlier (Table 4.2). Not only are several variables likely conveying similar information (e.g., 0-1- and 0-3-km SRH), but there might be many factors not captured by the environmental variables included in this study that affect storm-scale evolution and microphysical processes in supercell storms. It is hoped that the results of this study provide a foundation for the prediction of hailfall areal extent based on representative environmental conditions. Future work in this area may include adding to this dataset from after 2014 to continue trying to understand the microphysics of hail growth and hail variability more in depth, as well as breaking down the dataset to see if there are regional and seasonal differences in the response of hail areal extent to environmental variables, as different regions and seasons have different threshold values for several of the variables analyzed.
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